Machine Learning for Classification and Clustering of Dementia Data

Dementia is a term used to describe heterogeneous diseases that can generally be characterised by a decline in cognitive ability that affects daily living. It has been predicted that the prevalence of dementia will increase significantly over the coming years, thus it is a priority worldwide. This thesis discusses research conducted with two primary aims. They were to investigate the use of machine learning for distinguishing between people with and without dementia, as well as differentiating between key dementia subtypes where appropriate; and to gain an understanding of the inherent structure of dementia data, to ultimately investigate disease signatures. Data was acquired from the National Alzheimer's Coordinating Center in the United States, and a data set comprising 32,573 observations and 260 features of mixed type was utilised. It included features whose values were constrained by relations with others, as well as two types of missingness which arose when data was unexpectedly not recorded and when the information was irrelevant or unobtainable for a known reason, respectively. Notably, the former genuinely missing values were imputed where possible, whilst the latter conditionally missing values were handled. An imputation approach was developed, which simultaneously builds a random forest classifier while handling conditionally missing values. It maintained the known relations in the data set, so far as possible. A clustering approach was also developed that ultimately measures the similarity of observations based on the similarity of their paths through the trees of an isolation forest before employing spectral clustering. Crucially, it can naturally draw on variables of mixed type. A dementia classifier with an area under the receiver operating characteristic curve (AUC) of 0.99 and 10 pairwise dementia subtype classifiers with AUCs ranging from 0.88 to 1.0 (rounded) were produced, suggesting machine learning could be a useful tool for diagnosing dementia and differentiating between the main subtypes. Key features were identified using these classifiers and were markedly different for the two types of diagnosis. Furthermore, preliminary experiments conducted using the clustering approach suggested that mild cognitive impairment may be a mild form of dementia as opposed to a clinical entity, over which there is much debate; and there could be evidence for the current subtypes. Ultimately, these findings have the potential to transform the way dementia is diagnosed

Symphonic Winds, March 28, 2022

Center for the Performing Arts March 28, 2022 Monday Evening 8:00 p.m

Gerrymandering and computational redistricting

Partisan gerrymandering poses a threat to democracy. Moreover, the complexity of the districting task may exceed human capacities. One potential solution is using computational models to automate the districting process by optimizing objective and open criteria, such as how spatially compact districts are. We formulated one such model that minimised pairwise distance between voters within a district. Using US Census Bureau data, we confirmed our prediction that the difference in compactness between the computed and actual districts would be greatest for states that are large and, therefore, difficult for humans to properly district given their limited capacities. The computed solutions highlighted differences in how humans and machines solve this task with machine solutions more fully optimised and displaying emergent properties not evident in human solutions. These results suggest a division of labour in which humans debate and formulate districting criteria whereas machines optimise the criteria to draw the district boundaries. We discuss how criteria can be expanded beyond notions of compactness to include other factors, such as respecting municipal boundaries, historic communities, and relevant legislation

Ensemble Concerts: University Band and Symphonic Band, April 20, 2022

Center for the Performing Arts April 20, 2022 Wednesday Evening 8:00 p.m

Wind Symphony, September 30, 2021

Center for the Performing Arts September 30, 2021 Thursday Evening 8:00 p.m

University Band, Symphonic Band, Symphonic Winds, November 16, 2021

Center for the Performing Arts November 16, 2021 Tuesday Evening 7:00 p.m

Ensemble Concerts: University and Symphonic Band, April 26, 2023

Center for the Performing ArtsApril 26th, 2022Wednesday Evening8:00 p.m

Patient safety in acute care: are we going around in circles?

This article provides a critical discussion examining why adult patients continue to unnecessarily deteriorate and die despite repeated healthcare policy initiatives. After considering the policy background and reviewing current trends in the data, it proposes some solutions that, if enacted, would, the authors believe, have a direct impact on survival rates. Health professionals working in hospitals are failing to recognise signs of physiological deterioration. As a result, adult patients are dying unnecessarily, estimated to be in the region of 1000 a month. This is despite international healthcare policy requiring practitioners to be appropriately trained to recognise the deteriorating adult patient and to intervene. A literature review centred on health policy for England from 1999 to 2015 was undertaken, with reference to international policy and practice. This article also draws on the authors' combined clinical experience, which is underpinned by relevant research and theory. The implications for nursing could be significant. Change is urgently required otherwise people will continue to die unnecessarily. Health professionals, healthcare organisations and international governments working together can prevent unnecessary deaths from happening within acute hospitals